wcc: Agreement between patterns including peak shifts

In wccsom: SOM Networks for Comparing Patterns with Peak Shifts

Description

Weighted cross correlation and autocorrelation, as described in De Gelder et al. (2001), for assessing similarities in spectra-like data containing peak shifts. Euclidean distances are useless in this situation.

Usage

wcc(pattern1, pattern2, trwdth, wghts, acors)
wac(pattern1, trwdth, wghts)
wacmat(patterns, trwdth, wghts, do.transpose = TRUE)

Arguments

pattern1	Pattern.
pattern2	Another pattern.
patterns	Pattern matrix: rows correspond with patterns.
trwdth	Triangle width, given in the number of data points.
wghts	Optional weights vector, will be calculated from triangle width if necessary. Sometimes it is more efficient to pre-calculate it and give it as an argument.
acors	Autocorrelation, also optional to speed up calculations.
do.transpose	Internally, columns should correspond with patterns, so normally one should leave this value to its default: TRUE. If a matrix is already in the correct format, one can avoid unnecessary double transpose operations.

Value

Function wcc returns the WCC value, a similarity value between 0 and 1. Functions wac and wacmat return weighted autocorrelations for one pattern and a matrix of patterns, respectively.

Author(s)

Ron Wehrens

References

R. de Gelder, R. Wehrens, and J.A. Hageman. A generalized expression for the similarity spectra: application to powder diffraction pattern classification. J. Comput. Chem., 22(3), 273-289, 2001.

See Also

wccsom, wccxyf

Examples

data(cepha)
wac(cepha$patterns[1,], 20)
wacmat(t(cepha$patterns), 20)
wcc(cepha$patterns[1,], cepha$patterns[2,], 20)

wccsom documentation built on May 30, 2017, 12:52 a.m.